Why you should care

Treatment for everything from the flu to Ebola could become faster.

By Vignesh Ramachandran

The Daily DoseJUL 30 2015

If you ever wondered if anyone is really paying attention to your tweets (aside from the one you wrote when you were blackout drunk), take heart. Researchers led by the University of Arizona recently kept a close eye on millions of tweets that mentioned 19 keywords. Get your mind out of the gutter! These are scientists, people. The key words of interest included “sneezing,” “wheezing” and “inhaler.”

Somehow, researchers got it in their heads that breaking down certain health-related issues people happen to tweet about may be a predictor of a bigger health problem. “There’s lots of cues you can get from social media," says Sudha Ram, the lead researcher and a University of Arizona professor of management information systems and computer science, who thinks the information could be useful for not just ERs but for health care in general. So maybe you could run for the hills in Texas, where something in the air might, say, be causing numerous people to suffer asthma attacks and land in the emergency room.

There could be valuable information to help protect patients or large populations from certain outbreaks or disasters.

Is it outlandish to think that tweets or other things we babble about on social media save lives? Or least predict certain health patterns? In recent years, that’s exactly what everyone from the U.S. Centers for Disease Control and Prevention to Google has been trying to discover. And after a few hiccups, experts say they are getting closer than most realize. In fact, Ram’s team found that data from Twitter can be paired with Google search interests, air-quality sensor information and electronic medical records to predict asthma-related visits to the ER. They built their prediction model by combing public tweets over a three-month period, plucking out the location of the messages — whether explicitly stated or assumed — and using more than a dozen different keywords during the process. So far, their predictive model has 75 percent accuracy, Ram says, which she argues is “pretty good” but obviously not perfect in the real world.

While ER schedules are made weeks in advance, Ram believes hospitals could use intel like this to make better guesses at the types of patients who might come in, so the appropriate doctors and machinery are on hand at the right time. Other experts are in on a similar game: trying to get a handle on how social media can help hospitals and medical centers prepare for the worst. The CDC, for one, has held contests to challenge people to use technology to predict flu seasons. And Google has been using aggregated search data to estimate flu activity across U.S. states and cities on its “flu trends” site.

Scientists also have their eye on longer-term goals: Some of this technology could help in the fight to prevent certain illnesses in the future. Researchers from the University of Pennsylvania, the University of Melbourne and Northwestern University published a study earlier this year that found messages on Twitter can help predict the rates of heart disease in a community — even more so than common risk factors, such as whether or not someone smokes or is diabetic. They note that certain language patterns — like venting about a relationship gone bad or swearing in anger — emerge as risk factors, while more positive emotions have protective effects. (Official OZY warning: Be careful what you tweet!)

But Matthew Biggerstaff, an epidemiologist at the CDC, warns there still isn’t a good understanding of what behavior compels people to post about, say, flu symptoms on social media. And there can be limits to all of this data. For instance, some social scientists have found that Google’s tool has overestimated the number of flu cases in recent years. (Google did not respond to a request for comment.) Another major issue at hand: trust. Public health and government agencies are being cautious about which tools they use because they could hurt their reputations in the event a prediction about an epidemic turns out to be wrong, says Biggerstaff.

Even so, there could be valuable information to help protect patients or large populations from certain outbreaks or disasters. Earlier this year, the American Journal of Infection Control published a study noting that tweets about the Ebola outbreak in West Africa last year reached more than 60 million people — three days before announcements about the official outbreak.